impute_mean

Replace missing intensity values (<code>NA</code>) with the Feature mean of non-<code>NA</code> values. For example, if a Feature has the measured intensities <code>NA, 1, NA, 3, 2</code> in samples 1-5,
the intensities after <code>impute_mean()</code> would be <code>2, 1, 2, 3, 2</code>.

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'.

Yannik Schermer

metamorphr

Tidy and Streamlined Metabolomics Data Workflows

impute_mean function

<dl><dt>data</dt>
<dd>A tidy tibble created by <code>read_featuretable</code>.</dd></dl>

Arguments

Impute missing values by replacing them with the Feature mean — impute_mean

<dl>

<dt>data</dt>
<dd>A tidy tibble created by <code>read_featuretable</code>.</dd>

</dl>

impute_mean: Impute missing values by replacing them with the Feature mean

Description

Usage

Value

Arguments

Examples